We propose a novel, physics-based method for detecting multi-scale tubular features in ultrasound images. The detector is based on a Hessian-matrix eigenvalue method, but unlike previous work, our detector is guided by an optimal model of vessel-like structures with respect to the ultrasound-image formation process. Our method provides a voxel-wise probability map, along with estimates of the radii and orientations of the detected tubes. These results can then be used for further processing, including segmentation and enhanced volume visualization. Most Hessian-based algorithms, including the well-known Frangi filter, were developed for CTA or MRA; they implicitly assume symmetry about the vessel centerline. This is not consistent with ultrasound data. We overcome this limitation by introducing a novel filter that allows multi-scale estimation both with respect to the vessel's centerline and with respect to the vessel's border. We use manually-segmented ultrasound imagery from 35 patients to show that our method is superior to standard Hessian-based methods. We evaluate the performance of the proposed methods based on the sensitivity and specificity like measures, and finally demonstrate further applicability of our method to vascular ultrasound images of the carotid artery, as well as ultrasound data for abdominal aortic aneurysms.

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